README.md

---
license: cc-by-4.0
base_model:
- Qwen/Qwen2.5-14B
datasets:
- nvidia/OpenMathReasoning
language:
- en
tags:
- nvidia
- math
library_name: transformers
---

# <span style="color: #7FFF7F;">OpenMath-Nemotron-14B-Kaggle GGUF Models</span>


## <span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`e743cddb`](https://github.com/ggerganov/llama.cpp/commit/e743cddb60dc3a8815b9de7dd7d5c491e61b2259).





---

## <span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span>

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the `--tensor-type` option in `llama.cpp` to manually "bump" important layers to higher precision. You can see the implementation here:  
👉 [Layer bumping with llama.cpp](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py)

While this does increase model file size, it significantly improves precision for a given quantization level.

### **I'd love your feedback—have you tried this? How does it perform for you?**




---

<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;">
  Click here to get info on choosing the right GGUF model format
</a>

---



<!--Begin Original Model Card-->


# OpenMath-Nemotron-14B-Kaggle

OpenMath-Nemotron-14B-Kaggle is created by finetuning [Qwen/Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) on a subset of [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) dataset.
This model was used in our first place submission to the [AIMO-2 Kaggle competition](https://www.kaggle.com/competitions/ai-mathematical-olympiad-progress-prize-2/leaderboard)!

![Evaluation Results](./results.png)


OpenMath-Nemotron models achieve state-of-the-art results on popular mathematical benchmarks. We present metrics as pass@1 (maj@64) where pass@1
is an average accuracy across 64 generations and maj@64 is the result of majority voting. 
Please see our [paper](https://arxiv.org/abs/2504.16891) for more details on the evaluation setup.

| Model                         | AIME24 |  AIME25     |  HMMT-24-25     | HLE-Math    |
|-------------------------------|-----------------|-------|-------|-------------|
| DeepSeek-R1-Distill-Qwen-1.5B | 26.8 (60.0)     | 21.4 (36.7) | 14.2 (26.5) | 2.9 (5.0)   |
| [OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B) CoT   | 61.6 (80.0)     | 49.5 (66.7) | 39.9 (53.6) | 5.4 (5.4)   |
| [OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B) TIR   | 52.0 (83.3)     | 39.7 (70.0) | 37.2 (60.7) | 2.5 (6.2)   |
| + Self GenSelect              | 83.3            | 70.0  | 62.2  | 7.9         |
| + 32B GenSelect               | 83.3            | 70.0  | 62.8  | 8.3         |
| DeepSeek-R1-Distill-Qwen-7B  | 54.4 (80.0)     | 38.6 (53.3) | 30.6 (42.9) | 3.3 (5.2)   |
| [OpenMath-Nemotron-7B](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) CoT    | 74.8 (80.0)     | 61.2 (76.7) | 49.7 (57.7) | 6.6 (6.6)   |
| [OpenMath-Nemotron-7B](https://huggingface.co/nvidia/OpenMath-Nemotron-7B) TIR    | 72.9 (83.3)     | 57.5 (76.7) | 54.6 (66.3) | 7.8 (10.8)  |
| + Self GenSelect              | 86.7            | 76.7  | 68.4  | 11.5        |
| + 32B GenSelect               | 86.7            | 76.7  | 69.9  | 11.9        |
| DeepSeek-R1-Distill-Qwen-14B | 65.8 (80.0)     | 48.4 (60.0) | 40.1 (52.0) | 4.2 (4.8)   |
| [OpenMath-Nemotron-14B-MIX (kaggle)](https://huggingface.co/nvidia/OpenMath-Nemotron-14B-Kaggle) | 73.7 (86.7) | 57.9 (73.3) | 50.5 (64.8) | 5.7 (6.5)   |
| [OpenMath-Nemotron-14B](https://huggingface.co/nvidia/OpenMath-Nemotron-14B) CoT   | 76.3 (83.3)     | 63.0 (76.7) | 52.1 (60.7) | 7.5 (7.6)   |
| [OpenMath-Nemotron-14B](https://huggingface.co/nvidia/OpenMath-Nemotron-14B) TIR   | 76.3 (86.7)     | 61.3 (76.7) | 58.6 (70.9) | 9.5 (11.5)  |
| + Self GenSelect              | 86.7            | 76.7  | 72.4  | 14.1        |
| + 32B GenSelect               | 90.0            | 76.7  | 71.9  | 13.7        |
| QwQ-32B                       | 78.1 (86.7)     | 66.5 (76.7) | 55.9 (63.3) | 9.0 (9.5)   |
| DeepSeek-R1-Distill-Qwen-32B | 66.9 (83.3)     | 51.8 (73.3) | 39.9 (51.0) | 4.8 (6.0)   |
| [OpenMath-Nemotron-32B](https://huggingface.co/nvidia/OpenMath-Nemotron-32B) CoT   | 76.5 (86.7)     | 62.5 (73.3) | 53.0 (59.2) | 8.3 (8.3)   |
| [OpenMath-Nemotron-32B](https://huggingface.co/nvidia/OpenMath-Nemotron-32B) TIR   | 78.4 (93.3)     | 64.2 (76.7) | 59.7 (70.9) | 9.2 (12.5)  |
| + Self GenSelect              | 93.3            | 80.0  | 73.5  | 15.7        |
| DeepSeek-R1                   | 79.1 (86.7)     | 64.3 (73.3) | 53.0 (59.2) | 10.5 (11.4) |

## Reproducing our results

The pipeline we used to produce the data and models is fully open-sourced!

- [Code](https://github.com/NVIDIA/NeMo-Skills)
- [Models](https://huggingface.co/collections/nvidia/openmathreasoning-68072c0154a5099573d2e730)
- [Dataset](https://huggingface.co/datasets/nvidia/OpenMathReasoning)
- [Paper](https://arxiv.org/abs/2504.16891)

We provide [all instructions](https://nvidia.github.io/NeMo-Skills/openmathreasoning1/)
to fully reproduce our results, including data generation.

## How to use the models?

This model will always use code execution to solve math problems, so we highly recommend to run inference with our 
[reference implementation in NeMo-Skills](https://nvidia.github.io/NeMo-Skills/openmathreasoning1/evaluation/).

Please note that these models have not been instruction tuned on general data and thus might not provide good answers outside of math domain. 

## Citation

If you find our work useful, please consider citing us!

```bibtex
@article{moshkov2025aimo2,
  title   = {AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset},
  author  = {Ivan Moshkov and Darragh Hanley and Ivan Sorokin and Shubham Toshniwal and Christof Henkel and Benedikt Schifferer and Wei Du and Igor Gitman},
  year    = {2025},
  journal = {arXiv preprint arXiv:2504.16891}
}
```

## Additional information

### License/Terms of Use: <br> 

GOVERNING TERMS: Use of this model is governed by [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode.en). 
Additional Information: [Apache License Version 2.0](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B/blob/main/LICENSE).

### Deployment Geography: 

Global <br> 

### Use Case: <br> 

This model is intended to facilitate research in the area of mathematical reasoning.  

 

### Release Date:  <br> 

Huggingface 04/23/2025 <br> 

### Model Architecture: <br> 

**Architecture Type:** Transformer decoder-only language model  <br> 

**Network Architecture:** Qwen2.5 <br> 


**This model was developed based on Qwen2.5-1.5B <br> 

** This model has 1.5B of model parameters. <br> 

### Input: <br> 

**Input Type(s):** Text <br> 

**Input Format(s):** String <br> 

**Input Parameters:** One-Dimensional (1D) <br> 

**Other Properties Related to Input:** Context length up to 131,072 tokens <br> 

 

### Output: <br> 

**Output Type(s):** Text <br> 

**Output Format:** String <br> 

**Output Parameters:** One-Dimensional (1D) <br> 

**Other Properties Related to Output:** Context length up to 131,072 tokens <br> 

 

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br> 

 

### Software Integration : <br> 

**Runtime Engine(s):** <br> 

* Tensor RT / Triton <br> 

 

**Supported Hardware Microarchitecture Compatibility:** <br> 

* NVIDIA Ampere <br> 

* NVIDIA Hopper <br> 

 

**Preferred Operating System(s):** <br> 

* Linux <br> 

 

### Model Version(s): 

[OpenMath-Nemotron-1.5B](https://huggingface.co/nvidia/OpenMath-Nemotron-1.5B)

[OpenMath-Nemotron-7B](https://huggingface.co/nvidia/OpenMath-Nemotron-7B)

[OpenMath-Nemotron-14B](https://huggingface.co/nvidia/OpenMath-Nemotron-14B)

[OpenMath-Nemotron-32B](https://huggingface.co/nvidia/OpenMath-Nemotron-32B)


# Ethical Considerations: 

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. 

For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](./EXPLAINABILITY.md), [Bias](./BIAS.md), [Safety & Security](./SAFETY.md), and [Privacy](./PRIVACY.md) Subcards.

Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). 

<!--End Original Model Card-->

---

# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>

Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**:  

👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)  


The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder)

💬 **How to test**:  
 Choose an **AI assistant type**:  
   - `TurboLLM` (GPT-4.1-mini)  
   - `HugLLM` (Hugginface Open-source models)  
   - `TestLLM` (Experimental CPU-only)  

### **What I’m Testing**  
I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:  
- **Function calling** against live network services  
- **How small can a model go** while still handling:  
  - Automated **Nmap security scans**  
  - **Quantum-readiness checks**  
  - **Network Monitoring tasks**  

🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):  
- ✅ **Zero-configuration setup**  
- ⏳ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low.
- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!  

### **Other Assistants**  
🟢 **TurboLLM** – Uses **gpt-4.1-mini** :
- **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited. 
- **Create custom cmd processors to run .net code on Quantum Network Monitor Agents**
- **Real-time network diagnostics and monitoring**
- **Security Audits**
- **Penetration testing** (Nmap/Metasploit)  

🔵 **HugLLM** – Latest Open-source models:  
- 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

### 💡 **Example commands you could test**:  
1. `"Give me info on my websites SSL certificate"`  
2. `"Check if my server is using quantum safe encyption for communication"`  
3. `"Run a comprehensive security audit on my server"`
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution!

### Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful.

If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊